Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations2274
Missing cells18947
Missing cells (%)32.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory462.0 KiB
Average record size in memory208.1 B

Variable types

Numeric24
Text2

Alerts

Adwords Keywords is highly overall correlated with Adwords Traffic and 1 other fieldsHigh correlation
Adwords Traffic is highly overall correlated with Adwords Keywords and 1 other fieldsHigh correlation
Avg - Average Bounce Rate is highly overall correlated with Avg - Average Pages Per VisitHigh correlation
Avg - Average Pages Per Visit is highly overall correlated with Avg - Average Bounce Rate and 1 other fieldsHigh correlation
Avg - Average Time On Site is highly overall correlated with Avg - Average Pages Per VisitHigh correlation
Avg - Total Visits is highly overall correlated with Avg - real visits and 4 other fieldsHigh correlation
Avg - real visits is highly overall correlated with Avg - Total Visits and 1 other fieldsHigh correlation
Avg Total Users is highly overall correlated with Avg - Total Visits and 4 other fieldsHigh correlation
Employee Count is highly overall correlated with Organic Traffic and 1 other fieldsHigh correlation
Estimated Page Views is highly overall correlated with Organic Keywords and 3 other fieldsHigh correlation
Followers is highly overall correlated with PostsHigh correlation
Organic Keywords is highly overall correlated with Avg - Total Visits and 5 other fieldsHigh correlation
Organic Traffic is highly overall correlated with Avg - Total Visits and 6 other fieldsHigh correlation
PLA keywords is highly overall correlated with Adwords Keywords and 1 other fieldsHigh correlation
Posts is highly overall correlated with FollowersHigh correlation
Rank - Domain is highly overall correlated with Estimated Page Views and 3 other fieldsHigh correlation
Semrush Rank is highly overall correlated with Avg - Total Visits and 6 other fieldsHigh correlation
Tenure in Nuvemshop in months has 128 (5.6%) missing values Missing
Employee Count has 2180 (95.9%) missing values Missing
Current plan id has 125 (5.5%) missing values Missing
Estimated Page Views has 314 (13.8%) missing values Missing
Rank - Domain has 236 (10.4%) missing values Missing
Product Count - Domain has 402 (17.7%) missing values Missing
Estimated Sales - Domain has 251 (11.0%) missing values Missing
Combined Followers has 1664 (73.2%) missing values Missing
Number of technologies has 238 (10.5%) missing values Missing
Avg Total Users has 642 (28.2%) missing values Missing
Avg - Total Visits has 642 (28.2%) missing values Missing
Avg - Average Bounce Rate has 642 (28.2%) missing values Missing
Avg - Average Time On Site has 642 (28.2%) missing values Missing
Avg - Average Pages Per Visit has 642 (28.2%) missing values Missing
Avg - real visits has 826 (36.3%) missing values Missing
Followers has 499 (21.9%) missing values Missing
Posts has 499 (21.9%) missing values Missing
Semrush Rank has 1375 (60.5%) missing values Missing
Organic Keywords has 1375 (60.5%) missing values Missing
Organic Traffic has 1375 (60.5%) missing values Missing
Adwords Keywords has 1375 (60.5%) missing values Missing
Adwords Traffic has 1375 (60.5%) missing values Missing
PLA keywords has 1375 (60.5%) missing values Missing
Average ticket value has 125 (5.5%) missing values Missing
Estimated Page Views is highly skewed (γ1 = 29.40951419) Skewed
Product Count - Domain is highly skewed (γ1 = 41.11713089) Skewed
Combined Followers is highly skewed (γ1 = 21.38781164) Skewed
Avg Total Users is highly skewed (γ1 = 29.52502277) Skewed
Avg - Total Visits is highly skewed (γ1 = 34.29038332) Skewed
Avg - Average Pages Per Visit is highly skewed (γ1 = 21.51754552) Skewed
Avg - real visits is highly skewed (γ1 = 35.06910566) Skewed
Organic Traffic is highly skewed (γ1 = 21.55203981) Skewed
PLA keywords is highly skewed (γ1 = 29.26936695) Skewed
ID is uniformly distributed Uniform
ID has unique values Unique
Employee Count has 49 (2.2%) zeros Zeros
Combined Followers has 29 (1.3%) zeros Zeros
Avg - Average Bounce Rate has 213 (9.4%) zeros Zeros
Avg - Average Time On Site has 109 (4.8%) zeros Zeros
Adwords Keywords has 538 (23.7%) zeros Zeros
Adwords Traffic has 539 (23.7%) zeros Zeros
PLA keywords has 698 (30.7%) zeros Zeros

Reproduction

Analysis started2024-11-03 01:41:35.019573
Analysis finished2024-11-03 01:41:57.896548
Duration22.88 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Uniform  Unique 

Distinct2274
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1137.5
Minimum1
Maximum2274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:57.933096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile114.65
Q1569.25
median1137.5
Q31705.75
95-th percentile2160.35
Maximum2274
Range2273
Interquartile range (IQR)1136.5

Descriptive statistics

Standard deviation656.59158
Coefficient of variation (CV)0.57722337
Kurtosis-1.2
Mean1137.5
Median Absolute Deviation (MAD)568.5
Skewness0
Sum2586675
Variance431112.5
MonotonicityStrictly increasing
2024-11-02T22:41:57.985724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1519 1
 
< 0.1%
1513 1
 
< 0.1%
1514 1
 
< 0.1%
1515 1
 
< 0.1%
1516 1
 
< 0.1%
1517 1
 
< 0.1%
1518 1
 
< 0.1%
1520 1
 
< 0.1%
1511 1
 
< 0.1%
Other values (2264) 2264
99.6%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2274 1
< 0.1%
2273 1
< 0.1%
2272 1
< 0.1%
2271 1
< 0.1%
2270 1
< 0.1%
2269 1
< 0.1%
2268 1
< 0.1%
2267 1
< 0.1%
2266 1
< 0.1%
2265 1
< 0.1%

Tenure in Nuvemshop in months
Real number (ℝ)

Missing 

Distinct2103
Distinct (%)98.0%
Missing128
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean37.029799
Minimum3.5324126
Maximum137.96594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:58.038162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.5324126
5-th percentile9.8597153
Q120.326259
median34.713404
Q348.366385
95-th percentile77.150032
Maximum137.96594
Range134.43353
Interquartile range (IQR)28.040126

Descriptive statistics

Standard deviation21.652725
Coefficient of variation (CV)0.58473784
Kurtosis1.9219775
Mean37.029799
Median Absolute Deviation (MAD)13.980481
Skewness1.1468917
Sum79465.949
Variance468.84049
MonotonicityNot monotonic
2024-11-02T22:41:58.096206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 6
 
0.3%
14 4
 
0.2%
17 4
 
0.2%
40 4
 
0.2%
16 4
 
0.2%
15 3
 
0.1%
26 3
 
0.1%
39 3
 
0.1%
49 3
 
0.1%
18 3
 
0.1%
Other values (2093) 2109
92.7%
(Missing) 128
 
5.6%
ValueCountFrequency (%)
3.532412634 1
< 0.1%
4.6 1
< 0.1%
5.696120818 1
< 0.1%
5.7 1
< 0.1%
5.715164277 1
< 0.1%
5.9 1
< 0.1%
5.966599462 1
< 0.1%
6.136074149 1
< 0.1%
6.2 1
< 0.1%
6.236050627 1
< 0.1%
ValueCountFrequency (%)
137.965942 1
< 0.1%
137.1318537 1
< 0.1%
135.1444788 1
< 0.1%
131.0098555 1
< 0.1%
129.4904925 1
< 0.1%
129.3977834 1
< 0.1%
128.0823749 1
< 0.1%
124.5948678 1
< 0.1%
124.5149918 1
< 0.1%
123.2338463 1
< 0.1%
Distinct2227
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:58.210178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length8.9023747
Min length3

Characters and Unicode

Total characters20244
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2215 ?
Unique (%)97.4%

Sample

1st row433,616.40
2nd row188,149.19
3rd row209,656.76
4th row185,180.44
5th row265,657.11
ValueCountFrequency (%)
1000000 15
 
0.7%
400000 11
 
0.5%
500000 9
 
0.4%
600000 6
 
0.3%
5000000 3
 
0.1%
1500000 3
 
0.1%
1250000 2
 
0.1%
900000 2
 
0.1%
15000000 2
 
0.1%
20000000 2
 
0.1%
Other values (2217) 2219
97.6%
2024-11-02T22:41:58.378336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2174
10.7%
, 2090
10.3%
1 1935
9.6%
0 1795
8.9%
2 1788
8.8%
3 1667
8.2%
4 1545
7.6%
5 1537
7.6%
6 1524
7.5%
7 1464
7.2%
Other values (2) 2725
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20244
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2174
10.7%
, 2090
10.3%
1 1935
9.6%
0 1795
8.9%
2 1788
8.8%
3 1667
8.2%
4 1545
7.6%
5 1537
7.6%
6 1524
7.5%
7 1464
7.2%
Other values (2) 2725
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20244
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2174
10.7%
, 2090
10.3%
1 1935
9.6%
0 1795
8.9%
2 1788
8.8%
3 1667
8.2%
4 1545
7.6%
5 1537
7.6%
6 1524
7.5%
7 1464
7.2%
Other values (2) 2725
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20244
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2174
10.7%
, 2090
10.3%
1 1935
9.6%
0 1795
8.9%
2 1788
8.8%
3 1667
8.2%
4 1545
7.6%
5 1537
7.6%
6 1524
7.5%
7 1464
7.2%
Other values (2) 2725
13.5%

Employee Count
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct34
Distinct (%)36.2%
Missing2180
Missing (%)95.9%
Infinite0
Infinite (%)0.0%
Mean56.457447
Minimum0
Maximum1280
Zeros49
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:58.442691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314.5
95-th percentile316.05
Maximum1280
Range1280
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation174.25612
Coefficient of variation (CV)3.0865038
Kurtosis28.254811
Mean56.457447
Median Absolute Deviation (MAD)0
Skewness4.8714232
Sum5307
Variance30365.197
MonotonicityNot monotonic
2024-11-02T22:41:58.491545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 49
 
2.2%
1 5
 
0.2%
6 3
 
0.1%
3 3
 
0.1%
13 2
 
0.1%
7 2
 
0.1%
19 2
 
0.1%
12 2
 
0.1%
2 1
 
< 0.1%
511 1
 
< 0.1%
Other values (24) 24
 
1.1%
(Missing) 2180
95.9%
ValueCountFrequency (%)
0 49
2.2%
1 5
 
0.2%
2 1
 
< 0.1%
3 3
 
0.1%
4 1
 
< 0.1%
6 3
 
0.1%
7 2
 
0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
12 2
 
0.1%
ValueCountFrequency (%)
1280 1
< 0.1%
699 1
< 0.1%
534 1
< 0.1%
511 1
< 0.1%
318 1
< 0.1%
315 1
< 0.1%
277 1
< 0.1%
255 1
< 0.1%
193 1
< 0.1%
177 1
< 0.1%

Current plan id
Real number (ℝ)

Missing 

Distinct34
Distinct (%)1.6%
Missing125
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean53.225686
Minimum6
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:58.537243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile29
Q155
median56
Q356
95-th percentile56
Maximum56
Range50
Interquartile range (IQR)1

Descriptive statistics

Standard deviation8.4120364
Coefficient of variation (CV)0.15804468
Kurtosis10.549174
Mean53.225686
Median Absolute Deviation (MAD)0
Skewness-3.3696738
Sum114382
Variance70.762357
MonotonicityNot monotonic
2024-11-02T22:41:58.582262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
56 1461
64.2%
55 471
 
20.7%
29 50
 
2.2%
26 31
 
1.4%
52 28
 
1.2%
38 20
 
0.9%
14 17
 
0.7%
32 8
 
0.4%
31 7
 
0.3%
16 5
 
0.2%
Other values (24) 51
 
2.2%
(Missing) 125
 
5.5%
ValueCountFrequency (%)
6 3
 
0.1%
8 1
 
< 0.1%
10 2
 
0.1%
11 1
 
< 0.1%
12 2
 
0.1%
13 1
 
< 0.1%
14 17
0.7%
15 2
 
0.1%
16 5
 
0.2%
19 1
 
< 0.1%
ValueCountFrequency (%)
56 1461
64.2%
55 471
 
20.7%
53 1
 
< 0.1%
52 28
 
1.2%
50 4
 
0.2%
46 1
 
< 0.1%
44 1
 
< 0.1%
40 1
 
< 0.1%
39 1
 
< 0.1%
38 20
 
0.9%

Estimated Page Views
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct755
Distinct (%)38.5%
Missing314
Missing (%)13.8%
Infinite0
Infinite (%)0.0%
Mean18445.219
Minimum0
Maximum4820659
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:58.631204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile64
Q1667
median2305
Q38154.75
95-th percentile59412.15
Maximum4820659
Range4820659
Interquartile range (IQR)7487.75

Descriptive statistics

Standard deviation127041.11
Coefficient of variation (CV)6.8874816
Kurtosis1060.5656
Mean18445.219
Median Absolute Deviation (MAD)2068
Skewness29.409514
Sum36152630
Variance1.6139443 × 1010
MonotonicityNot monotonic
2024-11-02T22:41:58.686103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 40
 
1.8%
43 34
 
1.5%
64 31
 
1.4%
86 28
 
1.2%
129 24
 
1.1%
409 23
 
1.0%
818 22
 
1.0%
107 21
 
0.9%
172 21
 
0.9%
150 18
 
0.8%
Other values (745) 1698
74.7%
(Missing) 314
 
13.8%
ValueCountFrequency (%)
0 3
 
0.1%
21 40
1.8%
43 34
1.5%
64 31
1.4%
86 28
1.2%
107 21
0.9%
129 24
1.1%
150 18
0.8%
172 21
0.9%
193 11
 
0.5%
ValueCountFrequency (%)
4820659 1
< 0.1%
1678486 1
< 0.1%
846999 1
< 0.1%
718883 1
< 0.1%
680465 1
< 0.1%
659910 1
< 0.1%
652347 1
< 0.1%
650774 1
< 0.1%
531125 1
< 0.1%
493526 1
< 0.1%

Rank - Domain
Real number (ℝ)

High correlation  Missing 

Distinct2034
Distinct (%)99.8%
Missing236
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean3498290.3
Minimum13917
Maximum13602156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:58.737679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum13917
5-th percentile381282.85
Q11325566.2
median2864319.5
Q34479023
95-th percentile10003139
Maximum13602156
Range13588239
Interquartile range (IQR)3153456.8

Descriptive statistics

Standard deviation2934436.1
Coefficient of variation (CV)0.83882008
Kurtosis2.3512384
Mean3498290.3
Median Absolute Deviation (MAD)1562779.5
Skewness1.56502
Sum7.1295156 × 109
Variance8.6109154 × 1012
MonotonicityNot monotonic
2024-11-02T22:41:58.796100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5903597 2
 
0.1%
4293480 2
 
0.1%
11751454 2
 
0.1%
2983095 2
 
0.1%
5053175 1
 
< 0.1%
5089821 1
 
< 0.1%
1923272 1
 
< 0.1%
1160778 1
 
< 0.1%
3515943 1
 
< 0.1%
547204 1
 
< 0.1%
Other values (2024) 2024
89.0%
(Missing) 236
 
10.4%
ValueCountFrequency (%)
13917 1
< 0.1%
46225 1
< 0.1%
47701 1
< 0.1%
49303 1
< 0.1%
56102 1
< 0.1%
80015 1
< 0.1%
84128 1
< 0.1%
95418 1
< 0.1%
98660 1
< 0.1%
99076 1
< 0.1%
ValueCountFrequency (%)
13602156 1
< 0.1%
13601126 1
< 0.1%
13599738 1
< 0.1%
13592022 1
< 0.1%
13587584 1
< 0.1%
13586579 1
< 0.1%
13586482 1
< 0.1%
13581545 1
< 0.1%
13577449 1
< 0.1%
13498359 1
< 0.1%

Product Count - Domain
Real number (ℝ)

Missing  Skewed 

Distinct504
Distinct (%)26.9%
Missing402
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean2052.9311
Minimum1
Maximum2330597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:58.850102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q131
median134
Q3271
95-th percentile1379.85
Maximum2330597
Range2330596
Interquartile range (IQR)240

Descriptive statistics

Standard deviation54886.948
Coefficient of variation (CV)26.735894
Kurtosis1735.9489
Mean2052.9311
Median Absolute Deviation (MAD)109
Skewness41.117131
Sum3843087
Variance3.0125771 × 109
MonotonicityNot monotonic
2024-11-02T22:41:58.903430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 55
 
2.4%
1 52
 
2.3%
3 36
 
1.6%
5 33
 
1.5%
2 30
 
1.3%
14 28
 
1.2%
17 21
 
0.9%
27 20
 
0.9%
16 19
 
0.8%
12 18
 
0.8%
Other values (494) 1560
68.6%
(Missing) 402
 
17.7%
ValueCountFrequency (%)
1 52
2.3%
2 30
1.3%
3 36
1.6%
4 55
2.4%
5 33
1.5%
6 5
 
0.2%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 5
 
0.2%
ValueCountFrequency (%)
2330597 1
< 0.1%
432132 1
< 0.1%
114103 1
< 0.1%
45722 1
< 0.1%
36740 2
0.1%
33422 1
< 0.1%
32370 1
< 0.1%
31965 1
< 0.1%
30185 1
< 0.1%
24278 1
< 0.1%
Distinct749
Distinct (%)37.0%
Missing251
Missing (%)11.0%
Memory size17.9 KiB
2024-11-02T22:41:59.015050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length8.3939694
Min length1

Characters and Unicode

Total characters16981
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique473 ?
Unique (%)23.4%

Sample

1st row5,372.84
2nd row15,178.28
3rd row13,969.39
4th row12,787.36
5th row16,420.75
ValueCountFrequency (%)
500 78
 
3.7%
usd 75
 
3.6%
50 57
 
2.7%
644.74 31
 
1.5%
859.65 28
 
1.3%
1,289.48 24
 
1.1%
4,083.36 23
 
1.1%
5,104.20 22
 
1.0%
1,719.31 21
 
1.0%
1,074.56 21
 
1.0%
Other values (729) 1718
81.9%
2024-11-02T22:41:59.241095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1887
11.1%
, 1826
10.8%
1 1720
10.1%
4 1363
8.0%
3 1361
8.0%
2 1357
8.0%
5 1333
7.8%
0 1266
7.5%
6 1250
7.4%
7 1094
6.4%
Other values (7) 2524
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 1887
11.1%
, 1826
10.8%
1 1720
10.1%
4 1363
8.0%
3 1361
8.0%
2 1357
8.0%
5 1333
7.8%
0 1266
7.5%
6 1250
7.4%
7 1094
6.4%
Other values (7) 2524
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 1887
11.1%
, 1826
10.8%
1 1720
10.1%
4 1363
8.0%
3 1361
8.0%
2 1357
8.0%
5 1333
7.8%
0 1266
7.5%
6 1250
7.4%
7 1094
6.4%
Other values (7) 2524
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 1887
11.1%
, 1826
10.8%
1 1720
10.1%
4 1363
8.0%
3 1361
8.0%
2 1357
8.0%
5 1333
7.8%
0 1266
7.5%
6 1250
7.4%
7 1094
6.4%
Other values (7) 2524
14.9%

Combined Followers
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct487
Distinct (%)79.8%
Missing1664
Missing (%)73.2%
Infinite0
Infinite (%)0.0%
Mean26850.187
Minimum0
Maximum5015391
Zeros29
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:59.306940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q171.25
median694.5
Q36652.75
95-th percentile112390
Maximum5015391
Range5015391
Interquartile range (IQR)6581.5

Descriptive statistics

Standard deviation213256.09
Coefficient of variation (CV)7.9424435
Kurtosis494.88365
Mean26850.187
Median Absolute Deviation (MAD)693
Skewness21.387812
Sum16378614
Variance4.5478161 × 1010
MonotonicityNot monotonic
2024-11-02T22:41:59.362336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
1.3%
1 12
 
0.5%
2 6
 
0.3%
10 5
 
0.2%
11 4
 
0.2%
12 4
 
0.2%
6 4
 
0.2%
56 3
 
0.1%
37 3
 
0.1%
27 3
 
0.1%
Other values (477) 537
 
23.6%
(Missing) 1664
73.2%
ValueCountFrequency (%)
0 29
1.3%
1 12
0.5%
2 6
 
0.3%
3 2
 
0.1%
4 2
 
0.1%
5 2
 
0.1%
6 4
 
0.2%
7 3
 
0.1%
8 3
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
5015391 1
< 0.1%
1137607 1
< 0.1%
617573 1
< 0.1%
443700 1
< 0.1%
348154 1
< 0.1%
237164 1
< 0.1%
233600 1
< 0.1%
231100 1
< 0.1%
222000 1
< 0.1%
221100 1
< 0.1%

Number of technologies
Real number (ℝ)

Missing 

Distinct18
Distinct (%)0.9%
Missing238
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean6.0717092
Minimum0
Maximum18
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:59.406726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6
Q38
95-th percentile10
Maximum18
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8890652
Coefficient of variation (CV)0.47582405
Kurtosis-0.26451416
Mean6.0717092
Median Absolute Deviation (MAD)2
Skewness-0.031184843
Sum12362
Variance8.346698
MonotonicityNot monotonic
2024-11-02T22:41:59.448867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
6 421
18.5%
8 302
13.3%
7 283
12.4%
2 252
11.1%
9 187
8.2%
1 131
 
5.8%
3 105
 
4.6%
10 97
 
4.3%
4 91
 
4.0%
5 75
 
3.3%
Other values (8) 92
 
4.0%
(Missing) 238
10.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 131
 
5.8%
2 252
11.1%
3 105
 
4.6%
4 91
 
4.0%
5 75
 
3.3%
6 421
18.5%
7 283
12.4%
8 302
13.3%
9 187
8.2%
ValueCountFrequency (%)
18 1
 
< 0.1%
17 2
 
0.1%
15 5
 
0.2%
14 5
 
0.2%
13 10
 
0.4%
12 22
 
1.0%
11 46
 
2.0%
10 97
 
4.3%
9 187
8.2%
8 302
13.3%

Avg Total Users
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct1150
Distinct (%)70.5%
Missing642
Missing (%)28.2%
Infinite0
Infinite (%)0.0%
Mean8638.7782
Minimum1
Maximum2076913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:59.498633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile40.55
Q1521.75
median1132
Q36882.25
95-th percentile27900.05
Maximum2076913
Range2076912
Interquartile range (IQR)6360.5

Descriptive statistics

Standard deviation58446.454
Coefficient of variation (CV)6.7655926
Kurtosis994.00508
Mean8638.7782
Median Absolute Deviation (MAD)905
Skewness29.525023
Sum14098486
Variance3.415988 × 109
MonotonicityNot monotonic
2024-11-02T22:41:59.552316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 50
 
2.2%
562 30
 
1.3%
595 28
 
1.2%
480 20
 
0.9%
658 19
 
0.8%
355 17
 
0.7%
330 12
 
0.5%
299 11
 
0.5%
282 10
 
0.4%
191 10
 
0.4%
Other values (1140) 1425
62.7%
(Missing) 642
28.2%
ValueCountFrequency (%)
1 50
2.2%
2 8
 
0.4%
3 5
 
0.2%
11 2
 
0.1%
13 2
 
0.1%
15 1
 
< 0.1%
19 3
 
0.1%
23 2
 
0.1%
26 1
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
2076913 1
< 0.1%
881029 1
< 0.1%
395696 1
< 0.1%
203962 1
< 0.1%
182765 1
< 0.1%
173773 1
< 0.1%
143233 1
< 0.1%
131324 1
< 0.1%
115942 1
< 0.1%
112284 1
< 0.1%

Avg - Total Visits
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct1249
Distinct (%)76.5%
Missing642
Missing (%)28.2%
Infinite0
Infinite (%)0.0%
Mean16779.469
Minimum0
Maximum5997940
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:59.603323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile57.55
Q1594
median1644
Q39544.5
95-th percentile53361.45
Maximum5997940
Range5997940
Interquartile range (IQR)8950.5

Descriptive statistics

Standard deviation157600.62
Coefficient of variation (CV)9.3924673
Kurtosis1279.9992
Mean16779.469
Median Absolute Deviation (MAD)1403
Skewness34.290383
Sum27384094
Variance2.4837954 × 1010
MonotonicityNot monotonic
2024-11-02T22:41:59.657325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 38
 
1.7%
562 26
 
1.1%
595 22
 
1.0%
480 17
 
0.7%
2 15
 
0.7%
658 14
 
0.6%
299 11
 
0.5%
355 10
 
0.4%
282 9
 
0.4%
191 8
 
0.4%
Other values (1239) 1462
64.3%
(Missing) 642
28.2%
ValueCountFrequency (%)
0 3
 
0.1%
1 38
1.7%
2 15
 
0.7%
3 7
 
0.3%
4 2
 
0.1%
11 2
 
0.1%
20 1
 
< 0.1%
23 2
 
0.1%
28 1
 
< 0.1%
32 1
 
< 0.1%
ValueCountFrequency (%)
5997940 1
< 0.1%
1557615 1
< 0.1%
939257 1
< 0.1%
379247 1
< 0.1%
349199 1
< 0.1%
320642 1
< 0.1%
272617 1
< 0.1%
232353 1
< 0.1%
231220 1
< 0.1%
229432 1
< 0.1%

Avg - Average Bounce Rate
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct11
Distinct (%)0.7%
Missing642
Missing (%)28.2%
Infinite0
Infinite (%)0.0%
Mean0.48039216
Minimum0
Maximum1
Zeros213
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:59.703020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.3
median0.5
Q30.7
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.30388104
Coefficient of variation (CV)0.63256869
Kurtosis-0.84205854
Mean0.48039216
Median Absolute Deviation (MAD)0.2
Skewness0.087063495
Sum784
Variance0.092343684
MonotonicityNot monotonic
2024-11-02T22:41:59.744411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.5 286
12.6%
0 213
 
9.4%
1 195
 
8.6%
0.3 190
 
8.4%
0.7 164
 
7.2%
0.4 157
 
6.9%
0.6 142
 
6.2%
0.2 101
 
4.4%
0.8 92
 
4.0%
0.1 63
 
2.8%
(Missing) 642
28.2%
ValueCountFrequency (%)
0 213
9.4%
0.1 63
 
2.8%
0.2 101
 
4.4%
0.3 190
8.4%
0.4 157
6.9%
0.5 286
12.6%
0.6 142
6.2%
0.7 164
7.2%
0.8 92
 
4.0%
0.9 29
 
1.3%
ValueCountFrequency (%)
1 195
8.6%
0.9 29
 
1.3%
0.8 92
 
4.0%
0.7 164
7.2%
0.6 142
6.2%
0.5 286
12.6%
0.4 157
6.9%
0.3 190
8.4%
0.2 101
 
4.4%
0.1 63
 
2.8%

Avg - Average Time On Site
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct539
Distinct (%)33.0%
Missing642
Missing (%)28.2%
Infinite0
Infinite (%)0.0%
Mean194.81373
Minimum0
Maximum3829
Zeros109
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:59.791845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115
median115.5
Q3271
95-th percentile653.45
Maximum3829
Range3829
Interquartile range (IQR)256

Descriptive statistics

Standard deviation266.11368
Coefficient of variation (CV)1.3659904
Kurtosis34.655857
Mean194.81373
Median Absolute Deviation (MAD)111.5
Skewness4.1273977
Sum317936
Variance70816.49
MonotonicityNot monotonic
2024-11-02T22:41:59.848529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 115
 
5.1%
0 109
 
4.8%
2 52
 
2.3%
3 19
 
0.8%
4 18
 
0.8%
6 14
 
0.6%
5 14
 
0.6%
9 13
 
0.6%
17 11
 
0.5%
8 11
 
0.5%
Other values (529) 1256
55.2%
(Missing) 642
28.2%
ValueCountFrequency (%)
0 109
4.8%
1 115
5.1%
2 52
2.3%
3 19
 
0.8%
4 18
 
0.8%
5 14
 
0.6%
6 14
 
0.6%
7 7
 
0.3%
8 11
 
0.5%
9 13
 
0.6%
ValueCountFrequency (%)
3829 1
< 0.1%
3032 1
< 0.1%
1995 1
< 0.1%
1810 1
< 0.1%
1809 1
< 0.1%
1802 1
< 0.1%
1598 1
< 0.1%
1550 1
< 0.1%
1513 1
< 0.1%
1450 1
< 0.1%

Avg - Average Pages Per Visit
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct35
Distinct (%)2.1%
Missing642
Missing (%)28.2%
Infinite0
Infinite (%)0.0%
Mean4.778799
Minimum1
Maximum294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:59.897667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile12
Maximum294
Range293
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.016612
Coefficient of variation (CV)1.8867946
Kurtosis655.87181
Mean4.778799
Median Absolute Deviation (MAD)1
Skewness21.517546
Sum7799
Variance81.299293
MonotonicityNot monotonic
2024-11-02T22:41:59.946690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2 372
16.4%
1 278
12.2%
3 245
 
10.8%
4 215
 
9.5%
5 133
 
5.8%
6 97
 
4.3%
7 57
 
2.5%
8 42
 
1.8%
9 38
 
1.7%
12 30
 
1.3%
Other values (25) 125
 
5.5%
(Missing) 642
28.2%
ValueCountFrequency (%)
1 278
12.2%
2 372
16.4%
3 245
10.8%
4 215
9.5%
5 133
 
5.8%
6 97
 
4.3%
7 57
 
2.5%
8 42
 
1.8%
9 38
 
1.7%
10 23
 
1.0%
ValueCountFrequency (%)
294 1
< 0.1%
83 1
< 0.1%
57 1
< 0.1%
56 2
0.1%
54 1
< 0.1%
48 2
0.1%
40 1
< 0.1%
37 2
0.1%
35 1
< 0.1%
29 2
0.1%

Avg - real visits
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct1096
Distinct (%)75.7%
Missing826
Missing (%)36.3%
Infinite0
Infinite (%)0.0%
Mean10030.936
Minimum0
Maximum4189961
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:41:59.998864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.05
Q1334.75
median946.5
Q35282
95-th percentile28175.35
Maximum4189961
Range4189961
Interquartile range (IQR)4947.25

Descriptive statistics

Standard deviation113188.86
Coefficient of variation (CV)11.283977
Kurtosis1288.7538
Mean10030.936
Median Absolute Deviation (MAD)839.5
Skewness35.069106
Sum14524796
Variance1.2811718 × 1010
MonotonicityNot monotonic
2024-11-02T22:42:00.052259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 46
 
2.0%
595 22
 
1.0%
562 17
 
0.7%
658 16
 
0.7%
480 13
 
0.6%
299 9
 
0.4%
355 9
 
0.4%
191 7
 
0.3%
11253 5
 
0.2%
165 5
 
0.2%
Other values (1086) 1299
57.1%
(Missing) 826
36.3%
ValueCountFrequency (%)
0 5
 
0.2%
1 46
2.0%
2 3
 
0.1%
3 5
 
0.2%
4 2
 
0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
13 1
 
< 0.1%
19 3
 
0.1%
ValueCountFrequency (%)
4189961 1
< 0.1%
693606 1
< 0.1%
484531 1
< 0.1%
211140 1
< 0.1%
177230 1
< 0.1%
135434 1
< 0.1%
129052 1
< 0.1%
123269 1
< 0.1%
122905 1
< 0.1%
111730 1
< 0.1%

Followers
Real number (ℝ)

High correlation  Missing 

Distinct1755
Distinct (%)98.9%
Missing499
Missing (%)21.9%
Infinite0
Infinite (%)0.0%
Mean115826.04
Minimum14
Maximum6487873
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:42:00.102711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile1646.3
Q116614.5
median49354
Q3122319
95-th percentile432133.9
Maximum6487873
Range6487859
Interquartile range (IQR)105704.5

Descriptive statistics

Standard deviation267012.4
Coefficient of variation (CV)2.3052881
Kurtosis217.20301
Mean115826.04
Median Absolute Deviation (MAD)39923
Skewness11.548328
Sum2.0559123 × 108
Variance7.1295621 × 1010
MonotonicityNot monotonic
2024-11-02T22:42:00.215565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1859 2
 
0.1%
72405 2
 
0.1%
498113 2
 
0.1%
1338 2
 
0.1%
70025 2
 
0.1%
28112 2
 
0.1%
9479 2
 
0.1%
1178 2
 
0.1%
4720 2
 
0.1%
72551 2
 
0.1%
Other values (1745) 1755
77.2%
(Missing) 499
 
21.9%
ValueCountFrequency (%)
14 1
< 0.1%
74 1
< 0.1%
115 1
< 0.1%
122 1
< 0.1%
124 1
< 0.1%
139 1
< 0.1%
153 1
< 0.1%
154 1
< 0.1%
191 1
< 0.1%
210 1
< 0.1%
ValueCountFrequency (%)
6487873 1
< 0.1%
3796787 1
< 0.1%
2932529 1
< 0.1%
2110766 1
< 0.1%
1994559 1
< 0.1%
1944998 1
< 0.1%
1885509 1
< 0.1%
1642396 1
< 0.1%
1617847 1
< 0.1%
1524238 1
< 0.1%

Posts
Real number (ℝ)

High correlation  Missing 

Distinct1350
Distinct (%)76.1%
Missing499
Missing (%)21.9%
Infinite0
Infinite (%)0.0%
Mean2046.9375
Minimum1
Maximum98326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:42:00.269689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile70
Q1374
median932
Q32227
95-th percentile7435.7
Maximum98326
Range98325
Interquartile range (IQR)1853

Descriptive statistics

Standard deviation4036.442
Coefficient of variation (CV)1.971942
Kurtosis209.19796
Mean2046.9375
Median Absolute Deviation (MAD)713
Skewness10.871813
Sum3633314
Variance16292864
MonotonicityNot monotonic
2024-11-02T22:42:00.324006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
153 6
 
0.3%
147 5
 
0.2%
277 5
 
0.2%
590 5
 
0.2%
96 5
 
0.2%
70 4
 
0.2%
124 4
 
0.2%
80 4
 
0.2%
254 4
 
0.2%
498 4
 
0.2%
Other values (1340) 1729
76.0%
(Missing) 499
 
21.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 2
0.1%
4 1
< 0.1%
5 2
0.1%
7 1
< 0.1%
8 1
< 0.1%
9 2
0.1%
12 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
98326 1
< 0.1%
56928 1
< 0.1%
37226 1
< 0.1%
33673 1
< 0.1%
26637 1
< 0.1%
26239 1
< 0.1%
19292 1
< 0.1%
19103 1
< 0.1%
18540 1
< 0.1%
18032 1
< 0.1%

Semrush Rank
Real number (ℝ)

High correlation  Missing 

Distinct897
Distinct (%)99.8%
Missing1375
Missing (%)60.5%
Infinite0
Infinite (%)0.0%
Mean347997.84
Minimum465
Maximum8935575
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:42:00.376401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum465
5-th percentile9931.3
Q148662
median136687
Q3275904.5
95-th percentile1076149.6
Maximum8935575
Range8935110
Interquartile range (IQR)227242.5

Descriptive statistics

Standard deviation898977.26
Coefficient of variation (CV)2.583284
Kurtosis44.334977
Mean347997.84
Median Absolute Deviation (MAD)101958
Skewness6.2689718
Sum3.1285006 × 108
Variance8.0816012 × 1011
MonotonicityNot monotonic
2024-11-02T22:42:00.427967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
994265 2
 
0.1%
139771 2
 
0.1%
301292 1
 
< 0.1%
324774 1
 
< 0.1%
289427 1
 
< 0.1%
235997 1
 
< 0.1%
101420 1
 
< 0.1%
26824 1
 
< 0.1%
605688 1
 
< 0.1%
53875 1
 
< 0.1%
Other values (887) 887
39.0%
(Missing) 1375
60.5%
ValueCountFrequency (%)
465 1
< 0.1%
1929 1
< 0.1%
2196 1
< 0.1%
2275 1
< 0.1%
2401 1
< 0.1%
2470 1
< 0.1%
2890 1
< 0.1%
3051 1
< 0.1%
3154 1
< 0.1%
3300 1
< 0.1%
ValueCountFrequency (%)
8935575 1
< 0.1%
8824744 1
< 0.1%
7576269 1
< 0.1%
7496904 1
< 0.1%
7312415 1
< 0.1%
6938497 1
< 0.1%
6424105 1
< 0.1%
6367851 1
< 0.1%
6215601 1
< 0.1%
5752013 1
< 0.1%

Organic Keywords
Real number (ℝ)

High correlation  Missing 

Distinct712
Distinct (%)79.2%
Missing1375
Missing (%)60.5%
Infinite0
Infinite (%)0.0%
Mean2624.7008
Minimum1
Maximum251158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:42:00.478628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34.9
Q1192
median495
Q31637.5
95-th percentile10075.5
Maximum251158
Range251157
Interquartile range (IQR)1445.5

Descriptive statistics

Standard deviation10774.102
Coefficient of variation (CV)4.1048878
Kurtosis329.83843
Mean2624.7008
Median Absolute Deviation (MAD)387
Skewness15.784613
Sum2359606
Variance1.1608128 × 108
MonotonicityNot monotonic
2024-11-02T22:42:00.531390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14
 
0.6%
2 7
 
0.3%
3 5
 
0.2%
84 5
 
0.2%
97 4
 
0.2%
58 4
 
0.2%
184 4
 
0.2%
88 4
 
0.2%
311 4
 
0.2%
78 4
 
0.2%
Other values (702) 844
37.1%
(Missing) 1375
60.5%
ValueCountFrequency (%)
1 14
0.6%
2 7
0.3%
3 5
 
0.2%
4 2
 
0.1%
6 2
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
14 1
 
< 0.1%
17 1
 
< 0.1%
ValueCountFrequency (%)
251158 1
< 0.1%
107688 1
< 0.1%
76500 1
< 0.1%
63934 1
< 0.1%
48900 1
< 0.1%
47985 1
< 0.1%
43228 1
< 0.1%
41776 1
< 0.1%
35178 1
< 0.1%
34606 1
< 0.1%

Organic Traffic
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct817
Distinct (%)90.9%
Missing1375
Missing (%)60.5%
Infinite0
Infinite (%)0.0%
Mean13572.486
Minimum0
Maximum2004355
Zeros20
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:42:00.583812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23
Q1509.5
median1627
Q37000
95-th percentile55026.6
Maximum2004355
Range2004355
Interquartile range (IQR)6490.5

Descriptive statistics

Standard deviation74907.48
Coefficient of variation (CV)5.5190685
Kurtosis558.51185
Mean13572.486
Median Absolute Deviation (MAD)1463
Skewness21.55204
Sum12201665
Variance5.6111306 × 109
MonotonicityNot monotonic
2024-11-02T22:42:00.640503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
0.9%
1 4
 
0.2%
18 3
 
0.1%
229 3
 
0.1%
2 3
 
0.1%
110 3
 
0.1%
1197 2
 
0.1%
7 2
 
0.1%
4 2
 
0.1%
189 2
 
0.1%
Other values (807) 855
37.6%
(Missing) 1375
60.5%
ValueCountFrequency (%)
0 20
0.9%
1 4
 
0.2%
2 3
 
0.1%
3 2
 
0.1%
4 2
 
0.1%
5 1
 
< 0.1%
7 2
 
0.1%
8 2
 
0.1%
9 1
 
< 0.1%
18 3
 
0.1%
ValueCountFrequency (%)
2004355 1
< 0.1%
388535 1
< 0.1%
334611 1
< 0.1%
319859 1
< 0.1%
298791 1
< 0.1%
286729 1
< 0.1%
238243 1
< 0.1%
223710 1
< 0.1%
215719 1
< 0.1%
205276 1
< 0.1%

Adwords Keywords
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct101
Distinct (%)11.2%
Missing1375
Missing (%)60.5%
Infinite0
Infinite (%)0.0%
Mean16.389321
Minimum0
Maximum1247
Zeros538
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:42:00.695394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile68.3
Maximum1247
Range1247
Interquartile range (IQR)6

Descriptive statistics

Standard deviation73.845123
Coefficient of variation (CV)4.5056852
Kurtosis182.46677
Mean16.389321
Median Absolute Deviation (MAD)0
Skewness12.270322
Sum14734
Variance5453.1022
MonotonicityNot monotonic
2024-11-02T22:42:00.748821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 538
 
23.7%
1 45
 
2.0%
2 27
 
1.2%
3 24
 
1.1%
6 15
 
0.7%
4 14
 
0.6%
5 13
 
0.6%
8 12
 
0.5%
10 10
 
0.4%
7 8
 
0.4%
Other values (91) 193
 
8.5%
(Missing) 1375
60.5%
ValueCountFrequency (%)
0 538
23.7%
1 45
 
2.0%
2 27
 
1.2%
3 24
 
1.1%
4 14
 
0.6%
5 13
 
0.6%
6 15
 
0.7%
7 8
 
0.4%
8 12
 
0.5%
9 6
 
0.3%
ValueCountFrequency (%)
1247 1
< 0.1%
1190 1
< 0.1%
924 1
< 0.1%
376 1
< 0.1%
354 1
< 0.1%
267 1
< 0.1%
245 1
< 0.1%
232 1
< 0.1%
223 1
< 0.1%
215 1
< 0.1%

Adwords Traffic
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct317
Distinct (%)35.3%
Missing1375
Missing (%)60.5%
Infinite0
Infinite (%)0.0%
Mean1623.5984
Minimum0
Maximum246006
Zeros539
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:42:00.800620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3306
95-th percentile6917.6
Maximum246006
Range246006
Interquartile range (IQR)306

Descriptive statistics

Standard deviation10054.184
Coefficient of variation (CV)6.1925309
Kurtosis407.4558
Mean1623.5984
Median Absolute Deviation (MAD)0
Skewness18.165185
Sum1459615
Variance1.0108661 × 108
MonotonicityNot monotonic
2024-11-02T22:42:00.853298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 539
 
23.7%
12 5
 
0.2%
7 5
 
0.2%
9 5
 
0.2%
4 5
 
0.2%
6 4
 
0.2%
47 4
 
0.2%
61 4
 
0.2%
5 3
 
0.1%
11 3
 
0.1%
Other values (307) 322
 
14.2%
(Missing) 1375
60.5%
ValueCountFrequency (%)
0 539
23.7%
1 2
 
0.1%
2 1
 
< 0.1%
4 5
 
0.2%
5 3
 
0.1%
6 4
 
0.2%
7 5
 
0.2%
9 5
 
0.2%
11 3
 
0.1%
12 5
 
0.2%
ValueCountFrequency (%)
246006 1
< 0.1%
111008 1
< 0.1%
68560 1
< 0.1%
45982 1
< 0.1%
45945 1
< 0.1%
33630 1
< 0.1%
26297 1
< 0.1%
25655 1
< 0.1%
25621 1
< 0.1%
24350 1
< 0.1%

PLA keywords
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct54
Distinct (%)6.0%
Missing1375
Missing (%)60.5%
Infinite0
Infinite (%)0.0%
Mean11.428254
Minimum0
Maximum6014
Zeros698
Zeros (%)30.7%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:42:00.903502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile22
Maximum6014
Range6014
Interquartile range (IQR)0

Descriptive statistics

Standard deviation202.09582
Coefficient of variation (CV)17.683876
Kurtosis869.50394
Mean11.428254
Median Absolute Deviation (MAD)0
Skewness29.269367
Sum10274
Variance40842.722
MonotonicityNot monotonic
2024-11-02T22:42:01.004180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 698
30.7%
1 37
 
1.6%
2 26
 
1.1%
3 14
 
0.6%
6 9
 
0.4%
5 9
 
0.4%
10 8
 
0.4%
7 6
 
0.3%
13 6
 
0.3%
4 6
 
0.3%
Other values (44) 80
 
3.5%
(Missing) 1375
60.5%
ValueCountFrequency (%)
0 698
30.7%
1 37
 
1.6%
2 26
 
1.1%
3 14
 
0.6%
4 6
 
0.3%
5 9
 
0.4%
6 9
 
0.4%
7 6
 
0.3%
8 5
 
0.2%
9 2
 
0.1%
ValueCountFrequency (%)
6014 1
< 0.1%
493 1
< 0.1%
403 1
< 0.1%
220 1
< 0.1%
174 1
< 0.1%
108 1
< 0.1%
106 1
< 0.1%
104 1
< 0.1%
100 1
< 0.1%
95 1
< 0.1%

Average ticket value
Real number (ℝ)

Missing 

Distinct2102
Distinct (%)97.8%
Missing125
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean445.43399
Minimum10.58
Maximum11290.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.9 KiB
2024-11-02T22:42:01.056639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.58
5-th percentile100.84
Q1180.97
median262.68
Q3421.42
95-th percentile1285.47
Maximum11290.92
Range11280.34
Interquartile range (IQR)240.45

Descriptive statistics

Standard deviation717.82012
Coefficient of variation (CV)1.6115073
Kurtosis70.796577
Mean445.43399
Median Absolute Deviation (MAD)102.75
Skewness7.0457983
Sum957237.65
Variance515265.73
MonotonicityNot monotonic
2024-11-02T22:42:01.107989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
388.38 2
 
0.1%
204.9 2
 
0.1%
110.72 2
 
0.1%
449.64 2
 
0.1%
183.25 2
 
0.1%
376.07 2
 
0.1%
201.05 2
 
0.1%
227.33 2
 
0.1%
201.24 2
 
0.1%
124.8 2
 
0.1%
Other values (2092) 2129
93.6%
(Missing) 125
 
5.5%
ValueCountFrequency (%)
10.58 1
< 0.1%
17.95 1
< 0.1%
31.27 1
< 0.1%
32.96 1
< 0.1%
37.57 1
< 0.1%
38.28 1
< 0.1%
38.38 1
< 0.1%
39.04 1
< 0.1%
40 1
< 0.1%
45 1
< 0.1%
ValueCountFrequency (%)
11290.92 1
< 0.1%
10647.33 1
< 0.1%
8782.36 1
< 0.1%
7640.47 1
< 0.1%
6620.61 1
< 0.1%
6268.41 1
< 0.1%
6049.26 1
< 0.1%
5421.79 1
< 0.1%
5307.78 1
< 0.1%
5254.54 1
< 0.1%

Interactions

2024-11-02T22:41:56.444673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:35.283469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:36.193708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:37.144683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:37.924115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:38.823695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:39.745941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:40.657326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:41.566539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:42.452721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:43.364945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:44.270416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:45.181864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:46.118245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:47.103028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:48.000762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:48.913586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:49.871727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:50.747427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:51.679472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:52.637584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:53.604640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:54.576919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:55.528160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:56.477690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:35.317501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:36.227742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:37.173956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:37.956616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:38.856890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:39.780885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:40.689358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:41.599945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:42.487672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:43.397267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:44.304365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:45.264446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:46.155096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:47.137456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:48.033564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:48.947191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:49.904700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:50.781229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:51.715216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:52.676672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:53.640415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:54.612799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:55.564990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:56.517938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:35.355585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:36.266400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:37.204408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:37.993333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:38.898599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:39.819775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:40.727886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:41.634712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:42.526924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:43.434888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:44.344205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:45.302626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:46.196390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:47.176591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:48.077107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:48.984726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:49.942494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:50.820438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-02T22:41:44.981872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:45.923474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:46.899894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:47.806041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:48.725836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:49.675459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:50.556331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:51.487677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:52.391378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:53.398660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:54.378814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:55.285683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:56.249041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:57.237060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:36.049472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:36.994183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:37.795314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:38.685402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:39.594638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:40.500941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:41.421155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:42.266742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:43.211835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:44.122558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:45.026082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:45.967884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:46.943493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:47.848201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:48.766417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:49.718261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:50.598878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:51.530365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:52.433942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:53.443995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:54.421204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:55.328236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:56.292642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:57.277890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:36.085817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:37.034453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:37.826792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:38.722193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:39.633324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:40.541636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:41.458194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:42.302002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:43.251017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:44.160597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:45.065470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:46.007075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:46.983163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:47.886612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:48.802427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:49.757625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:50.637469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:51.568219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:52.473036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:53.484893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:54.460377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:55.366796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:56.331192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:57.314176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:36.121899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:37.070941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:37.857167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:38.756290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:39.669969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:40.580420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:41.493798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:42.383554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:43.289648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:44.196027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:45.103896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:46.045059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:47.025023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:47.924686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:48.837317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:49.797399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:50.675470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:51.605371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:52.559223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:53.526253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:54.499532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:55.452388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:56.369505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:57.349238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:36.159008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:37.106953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:37.889944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:38.789083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:39.708654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:40.619579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:41.531931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:42.419423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:43.329404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:44.234917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:45.143326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:46.082828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:47.065203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:47.963358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:48.875191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:49.836121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:50.712233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:51.643080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:52.598216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:53.566892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:54.538943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:55.492397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T22:41:56.407493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-02T22:42:01.156718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Adwords KeywordsAdwords TrafficAverage ticket valueAvg - Average Bounce RateAvg - Average Pages Per VisitAvg - Average Time On SiteAvg - Total VisitsAvg - real visitsAvg Total UsersCombined FollowersCurrent plan idEmployee CountEstimated Page ViewsFollowersIDNumber of technologiesOrganic KeywordsOrganic TrafficPLA keywordsPostsProduct Count - DomainRank - DomainSemrush RankTenure in Nuvemshop in months
Adwords Keywords1.0000.9910.0260.106-0.0450.1230.2230.2080.255-0.019-0.1130.0850.3550.0080.0100.4290.3870.3940.6450.0090.202-0.392-0.3940.067
Adwords Traffic0.9911.0000.0150.102-0.0470.1180.2280.2100.260-0.017-0.1160.1030.3630.0190.0140.4240.3930.4020.6390.0170.200-0.396-0.4020.062
Average ticket value0.0260.0151.0000.002-0.0030.0250.0250.0300.027-0.043-0.0020.1710.1050.070-0.2870.075-0.063-0.0980.0480.0550.034-0.1290.0980.075
Avg - Average Bounce Rate0.1060.1020.0021.000-0.686-0.3580.0120.1170.039-0.0530.0330.0130.063-0.0670.1250.0520.1620.1100.1260.0070.054-0.070-0.1100.042
Avg - Average Pages Per Visit-0.045-0.047-0.003-0.6861.0000.5690.1430.0160.0910.013-0.040-0.1630.0450.091-0.1670.007-0.017-0.010-0.0900.0010.037-0.0230.010-0.019
Avg - Average Time On Site0.1230.1180.025-0.3580.5691.0000.3790.2660.3280.008-0.022-0.0800.2250.068-0.1580.0780.2140.2390.107-0.0400.074-0.197-0.2390.034
Avg - Total Visits0.2230.2280.0250.0120.1430.3791.0000.9280.9620.074-0.0040.0260.4610.071-0.1910.1680.5240.5340.227-0.0210.147-0.419-0.5340.101
Avg - real visits0.2080.2100.0300.1170.0160.2660.9281.0000.9070.047-0.0130.0630.4310.011-0.1410.1240.4990.4840.234-0.0340.158-0.380-0.4840.096
Avg Total Users0.2550.2600.0270.0390.0910.3280.9620.9071.0000.082-0.018-0.0240.4940.068-0.1730.1840.5580.5680.255-0.0150.159-0.448-0.5680.106
Combined Followers-0.019-0.017-0.043-0.0530.0130.0080.0740.0470.0821.000-0.0610.3720.1330.386-0.2180.076-0.0130.017-0.0580.2480.063-0.116-0.0160.076
Current plan id-0.113-0.116-0.0020.033-0.040-0.022-0.004-0.013-0.018-0.0611.0000.3580.0130.0150.1680.068-0.096-0.095-0.0740.0760.077-0.0540.0950.494
Employee Count0.0850.1030.1710.013-0.163-0.0800.0260.063-0.0240.3720.3581.0000.4430.012-0.0980.1600.4630.5250.0720.0710.245-0.387-0.525-0.255
Estimated Page Views0.3550.3630.1050.0630.0450.2250.4610.4310.4940.1330.0130.4431.0000.285-0.2570.3160.7780.9130.3390.1540.320-0.844-0.9130.223
Followers0.0080.0190.070-0.0670.0910.0680.0710.0110.0680.3860.0150.0120.2851.000-0.3910.2010.0890.161-0.0520.6190.172-0.238-0.1610.172
ID0.0100.014-0.2870.125-0.167-0.158-0.191-0.141-0.173-0.2180.168-0.098-0.257-0.3911.000-0.1740.0700.0430.070-0.1470.0090.188-0.0430.160
Number of technologies0.4290.4240.0750.0520.0070.0780.1680.1240.1840.0760.0680.1600.3160.201-0.1741.0000.2980.2870.3010.1180.176-0.315-0.2870.129
Organic Keywords0.3870.393-0.0630.162-0.0170.2140.5240.4990.558-0.013-0.0960.4630.7780.0890.0700.2981.0000.8440.3970.0450.429-0.697-0.8440.084
Organic Traffic0.3940.402-0.0980.110-0.0100.2390.5340.4840.5680.017-0.0950.5250.9130.1610.0430.2870.8441.0000.3690.0660.308-0.785-1.0000.127
PLA keywords0.6450.6390.0480.126-0.0900.1070.2270.2340.255-0.058-0.0740.0720.339-0.0520.0700.3010.3970.3691.000-0.0400.194-0.347-0.3690.044
Posts0.0090.0170.0550.0070.001-0.040-0.021-0.034-0.0150.2480.0760.0710.1540.619-0.1470.1180.0450.066-0.0401.0000.291-0.181-0.0660.247
Product Count - Domain0.2020.2000.0340.0540.0370.0740.1470.1580.1590.0630.0770.2450.3200.1720.0090.1760.4290.3080.1940.2911.000-0.303-0.3080.278
Rank - Domain-0.392-0.396-0.129-0.070-0.023-0.197-0.419-0.380-0.448-0.116-0.054-0.387-0.844-0.2380.188-0.315-0.697-0.785-0.347-0.181-0.3031.0000.785-0.258
Semrush Rank-0.394-0.4020.098-0.1100.010-0.239-0.534-0.484-0.568-0.0160.095-0.525-0.913-0.161-0.043-0.287-0.844-1.000-0.369-0.066-0.3080.7851.000-0.127
Tenure in Nuvemshop in months0.0670.0620.0750.042-0.0190.0340.1010.0960.1060.0760.494-0.2550.2230.1720.1600.1290.0840.1270.0440.2470.278-0.258-0.1271.000

Missing values

2024-11-02T22:41:57.414703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-02T22:41:57.551997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-02T22:41:57.695484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDTenure in Nuvemshop in monthsMonthly SalesEmployee CountCurrent plan idEstimated Page ViewsRank - DomainProduct Count - DomainEstimated Sales - DomainCombined FollowersNumber of technologiesAvg Total UsersAvg - Total VisitsAvg - Average Bounce RateAvg - Average Time On SiteAvg - Average Pages Per VisitAvg - real visitsFollowersPostsSemrush RankOrganic KeywordsOrganic TrafficAdwords KeywordsAdwords TrafficPLA keywordsAverage ticket value
014.6433,616.40NaN55.0861.03697577.0135.05,372.84NaN1.0828.02679.00.2516.010.0432.0358082.02348.0301292.0240.0436.00.00.00.0486.95
126.2188,149.19NaN55.02434.03863622.045.015,178.28NaN6.074.074.00.02.02.0NaN48770.07186.0262338.0133.0558.00.00.00.0198.98
236.9209,656.76NaN56.02801.0942489.053.013,969.39NaN1.01261.01923.00.1439.04.0243.021618.0323.0161856.0229.01251.00.00.00.02082.23
346.8185,180.44NaN56.02564.01354704.04.012,787.36NaN6.02.03.00.362.04.01.0NaNNaN4702229.03.00.00.00.00.0317.88
457.8265,657.11NaN56.03512.03097806.028.016,420.75NaN7.0895.01334.00.244.04.0299.0498730.0141.098046.0155.02674.00.00.00.0252.06
568.5170,486.24NaN56.011333.0801251.0237.038,859.09714.08.01200.05894.00.7364.07.03909.0493820.01929.063564.01432.04896.03.0417.00.0269.06
678.6207,602.71NaN56.086.06178595.030.0859.65NaN8.0305.0375.00.5138.05.0188.064533.01141.0913972.0423.037.00.00.00.0592.06
788.1735,337.27NaN13.02068.03016304.064.012,894.8255.08.037395.045627.00.6198.04.025103.0109670.0590.0144898.01155.01489.0166.025621.073.0845.53
897.4169,323.52NaN29.01055.03369659.0654.06,581.73NaN2.08837.026530.00.5176.04.012827.0NaNNaN297103.01836.0447.00.00.00.0186.55
9105.7529,441.42NaN38.041886.0827878.042.091,392.08NaN6.057344.095370.00.4274.04.035509.0208011.0604.028668.01898.014232.05.0353.00.0356.23
IDTenure in Nuvemshop in monthsMonthly SalesEmployee CountCurrent plan idEstimated Page ViewsRank - DomainProduct Count - DomainEstimated Sales - DomainCombined FollowersNumber of technologiesAvg Total UsersAvg - Total VisitsAvg - Average Bounce RateAvg - Average Time On SiteAvg - Average Pages Per VisitAvg - real visitsFollowersPostsSemrush RankOrganic KeywordsOrganic TrafficAdwords KeywordsAdwords TrafficPLA keywordsAverage ticket value
22642265NaN4000000.0NaN39624.0408285.01124.0USD $86,455.780.06.026768.027001.00.786.02.018040.0463584.02860.025959.08146.016171.00.00.00.0NaN
22652266NaN15000000315.0NaN1678486.046225.02.0USD $1,307,951.812415.06.06092.06252.00.523.03.03065.0NaNNaN2401.063934.0298791.00.00.00.0NaN
22662267NaN1000000NaNNaNNaNNaNNaNNaNNaNNaN3210.03429.00.7305.03.02322.0NaNNaN9017.012067.061857.00.00.00.0NaN
22672268NaN1300000NaNNaNNaNNaNNaNNaNNaNNaN81435.0120507.00.4702.07.049569.0NaNNaN90108.0269.02980.015.0355.00.0NaN
22682269NaN112000013.0NaN4050.0985311.0238.0USD $17,676.650.010.017338.019368.00.5172.03.09725.089051.02041.0128847.0974.01757.00.00.00.0NaN
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